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Found 3 papers, 1 papers with code
Answer Generation for Retrieval-based Question Answering Systems
Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results.
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation
Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members.
